Data Engineer

• Design, build, and operate batch and streaming data pipelines on modern cloud data platforms.
• Develop robust ETL/ELT processes using SQL, Python, and PySpark.
• Implement layered / medallion data architectures and analytics-ready data models.
• Lead the implementation and evolution of data governance practices.
• Define and operationalize governance standards for data products and AI/ML features.
• Design and build AI agents and intelligent automation workflows.

Senior Data Engineer, Governance Focus

• Design, implement, and maintain logical and physical data structures within the MIR Data Layer
• Build and evolve metadata-driven schemas supporting: Document metadata, Transaction and workflow state, Event based and audit data, Reporting and analytics use cases
• Implement data solutions that support large-scale, concurrent workloads
• Optimize schemas, indexes, and access patterns to improve throughput and latency
• Leverage Memory-Optimized Tables (In-Memory OLTP) for performance-critical paths where appropriate, under architectural guidance
• Implement and support event-driven data processing within the MIR Data Layer
• Work with SQL Service Broker queues and services, including: Asynchronous processing, Status tracking and reconciliation, Retry and error-handling patterns
• Help ensure event processing is idempotent, traceable, and operationally reliable
• Build data structures that enable enterprise reporting and analytics without impacting transactional workloads
• Support snapshotting and reporting-friendly schemas for downstream consumers
• Assist in ensuring audit data is accurate, consistent, and compliant with regulatory expectations
• Contribute to cloud-readiness efforts for the MIR Data Layer
• Support migration and proof-of-concept work targeting: Amazon RDS for SQL Server, Azure SQL Database / Azure SQL Managed Instance
• Help adapt data designs to managed-platform constraints while preserving performance and correctness
• Participate in production readiness, stability, and incident resolution for the MIR Data Layer
• Debug and resolve complex production issues, including: Performance degradation, Blocking, deadlocks, and resource contention, Event based or Service Broker processing failures
• Partner closely with DBAs, SRE, and platform teams to improve monitoring and alerting, Support HA/DR and failover scenarios, Reduce repeat data-layer incidents
• Participate in on-call rotations or escalation coverage as appropriate for a senior-level role
• Ensure new data-layer implementations meet operability and supportability standards before release

Senior Data Engineer

• Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.
• Improve metadata-driven Azure Data Factory and Databricks patterns for orchestration, configuration, monitoring, restartability, and operational support.
• Develop reusable accelerators including CI/CD templates, Databricks Asset Bundle patterns, deployment automation, environment configuration, and data product onboarding templates.
• Design, develop, and support dbt models, macros, tests, documentation, and transformation standards for governed analytical data products.
• Provide guidance on appropriate technology selection and implementation patterns across dbt, Databricks notebooks and workflows, Delta Live Tables, Spark, and Starburst/Trino.
• Support cross-domain analytics initiatives by transforming source-refined data into trusted, reusable, business-aligned derivative data products.
• Leverage Unity Catalog to establish and support governed catalogs, schemas, tables, lineage, access controls, naming standards, and certification practices.
• Support Starburst/Trino as an analytical and federated query layer for governed enterprise data consumption.
• Apply Azure DevOps, Git, CI/CD, and Infrastructure as Code (IaC) practices to create repeatable, testable, and environment-aware platform delivery processes.
• Troubleshoot and resolve production issues related to orchestration, transformations, data quality, access management, query performance, deployments, and operational workflows.
• Collaborate with data engineering, analytics, platform, governance, and business teams to establish reusable, scalable, and supportable data engineering patterns.
• Contribute to the evolution of enterprise data engineering standards, governance practices, observability capabilities, and AI-ready data product frameworks.

Senior Artificial Intelligence Data Engineer

• Own and evolve the enterprise customer domain model across conceptual, logical, canonical, and physical layers
• Define canonical customer entities, identifiers, hierarchies, and relationships across CRM, billing, CIS, and downstream systems
• Govern model evolution to support new business capabilities while preventing redundancy and fragmentation
• Resolve semantic conflicts and enforce consistent customer definitions enterprise‑wide
• Own customer‑centric data products including curated datasets, views, and domain data assets
• Define and maintain data product contracts (schemas, SLAs, quality rules, access patterns, and usage expectations)
• Partner with engineering and analytics teams to ensure data products are production‑ready, discoverable, and reusable
• Design customer data products to support analytics, reporting, and AI/ML use cases
• Ensure alignment with enterprise data platform standards (lakehouse, catalogs, governance frameworks)
• Guide ingestion, transformation, and persistence patterns to preserve model integrity and performance
• Collaborate with application, integration, and analytics architects to embed consistent customer data across solutions
• Define and enforce data quality standards, validation rules, and stewardship processes
• Partner with data governance teams on metadata, lineage, classification, and access controls
• Enable downstream teams through clear documentation, diagrams, and reference models
• Act as a subject‑matter expert for customer data during design reviews and delivery engagements

Senior Data Architect – Customer Domain

• Own the full lifecycle of partner data as it moves through our systems
• Balance partner trust, engineering velocity, and long-term data platform health while enabling intelligent, contract-driven data exchange
• Guide data architecture decisions that incorporate AI-augmented capabilities into workflows for partner integrations
• Partner with Product, Engineering, and partner teams to develop flexible data roadmaps
• Drive pipeline improvements that scale across diverse partner data formats
• Maintain adaptable data contracts and schema strategies
• Identify and drive cross-platform improvements that enhance partner and developer experiences
• Collaborate across Engineering, Product, and partner teams to deliver AI-first, integration-ready data solutions
• Own ambiguous data challenges and drive them to resolution
• Mentor engineers and analytics contributors through coaching and feedback
• Ensure new partnerships are delivered with care, reliability, and ingenuity.

Data Operations Engineer

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Lead the design and stewardship of BSI's data governance architecture, data traceability standards, and quality frameworks, alongside oversight of reference and shared data management — with heightened emphasis on data integrity as a critical enabler of AI/ML model training and regulatory reporting.
• Establish and maintain enterprise standards for data lineage, data quality measurement, and governance processes, developed in close partnership with data governance stakeholders across the organization.
• Responsible for architecting reference and shared data patterns that underpin all business domains — Management Liability, Surety, and Claims — alongside the Analytical domain, ensuring a unified and governed approach to shared data assets across the enterprise.
• Collaborate with Enterprise teams to establish guidelines for AI-ready data, while identifying gaps and defining a clear roadmap to advance BSI's overall data readiness.
• Collaborate closely with the Data Engineering Value Stream to drive data governance implementation and engage with the Enterprise teams to align on and advance cross-enterprise data standards.
• Ensure data architecture decisions support and align with regulatory compliance requirements.
• Partner with senior leaders across Technology and Business to define architecture priorities, align on strategic direction, and ensure architectural decisions are firmly grounded in business imperatives.
• Collaborate with Enterprise architecture and center of excellence teams to define and advance architectural patterns aligned with modern principles; producing best practices, reference materials, and instructional videos that drive consistent adoption across the organization.
• Develop architecture designs and patterns, partnering with engineering teams to ensure effective execution and delivery.
• Build and sustain close working relationships with engineering teams, ensuring the right skills are developed and aligned to support evolving technology direction.
• Champions modern architecture principles and technology modernization, bringing clear thought leadership and a forward-looking perspective to drive meaningful change.
• Proactively identifies opportunities to leverage emerging technology advances by analyzing key industry and technology trends and assessing their potential impact on the enterprise.
• Drive the design and execution of technology proof of concepts, ensuring modern architecture solutions are rigorously assessed for feasibility and fit before implementation.

Data Architect – Data and Analytics

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Define and drive enterprise data architecture strategy
• Establish target-state data platform architectures and standards aligned to business priorities.
• Translate business requirements into scalable data architecture solutions.
• Design and govern enterprise data ecosystems
• Architect data solutions and integration strategies across systems.
• Ensure consistent access, integration, and consumption of data across platforms.
• Lead data architecture governance and quality initiatives
• Define standards for data quality, metadata, master data, and lifecycle management.
• Establish data governance policies, compliance controls, and security practices.
• Drive platform and technology decisions
• Evaluate and recommend data platforms, tools, and emerging technologies.
• Define standards for data integration, pipelines, and data services.
• Collaborate across business, data, and technology teams
• Partner with stakeholders to align data architecture with enterprise objectives.
• Act as a bridge between data strategy and technical execution.
• Provide leadership and oversight
• Lead architecture reviews and ensure adherence to standards
• Support delivery teams through the product lifecycle.

Enterprise Data Architect

• Build and operationalize complex data solutions, correct problems, apply transformations, and recommending data cleansing/quality solutions.
• Design complex data solutions
• Perform analysis of complex sources to determine value and use and recommend data to include in analytical processes.
• Incorporate core data management competencies including data governance, data security and data quality.
• Collaborate within and across teams to support delivery and educate end users on complex data products/analytic environment.
• Perform data and system analysis, assessment and resolution for complex defects and incidents and correct as appropriate.
• Test data movement, transformation code, and data components.
• Perform other duties as assigned.

Data Engineer II – AWS, Databricks, SQL, GenAI

• Design and deliver scalable data pipelines in AWS and Databricks that drive reliable, production-ready data solutions
• Proactively monitor, troubleshoot, and resolve data pipeline issues to ensure consistent system performance and data integrity
• Triage and resolve incidents based on business impact, ensuring timely and effective issue resolution
• Partner with business stakeholders to investigate and explain data discrepancies, improving trust in data assets
• Develop ad hoc analyses and reports using SQL and Python to support critical business decisions
• Automate manual processes to improve efficiency, reduce errors, and accelerate delivery timelines
• Collaborate across engineering, platform, and product teams to manage dependencies and resolve service issues
• Deliver solutions within an Agile (Kanban/DevOps) environment, contributing to continuous improvement and faster delivery cycles

Software Engineering Advisor – Data Engineer

• Lead the architecture, development, design, and maintenance of data pipelines and dashboards
• Partner with senior stakeholders to define reporting needs and deliver actionable insights
• Champion and implement advanced AI and machine learning techniques for reporting solutions
• Conduct in-depth analysis of large datasets to uncover critical trends and anomalies
• Establish best practices for data integrity and accuracy, mentoring team members
• Provide expert guidance on reporting tools, analytical methodologies, and emerging technologies
• Drive continuous improvement initiatives and contribute to technical roadmap
• Articulate complex findings to executive stakeholders through presentations and reports
• Lead data projects from conception through deployment aligning with business objectives
• Act as subject matter expert for data analysis and reporting

Lead Data Engineer

• Design, configure, and maintain Teamcenter data models using BMIDE, including object types, attributes, relationships, and business rules.
• Develop, deploy, and support BMIDE templates and extensions across development and production environments.
• Translate Navy engineering, logistics, and lifecycle management requirements into scalable, maintainable PLM data structures.
• Apply and enforce data modeling standards, naming conventions, and BMIDE best practices to ensure consistency, reusability, and compliance across the NPLM solution.
• Develop and maintain comprehensive data documentation, including data dictionaries, schema diagrams, and metadata definitions, to support transparency, governance, and knowledge transfer.
• Support the evolution of the Teamcenter data model architecture, ensuring alignment with enterprise objectives and interoperability requirements.
• Conduct impact analysis and implement controlled updates to the data model to support system integrations and evolving mission needs.
• Collaborate with solution architects, developers, and functional stakeholders to align data model design with system capabilities and business requirements.
• Partner with data governance and architecture teams to promote a single source of truth, reduce data duplication, and align with enterprise data standards.
• Support testing, validation, and troubleshooting of BMIDE configurations, including resolving schema conflicts, deployment issues, and data inconsistencies.
• Performs other duties as assigned.

Data Architect

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• Collaborate with software developers, data scientists, and product managers to understand requirements, gather specifications and align on project goals.
• Contribute technical expertise in data engineering to design and implement data solutions.
• Participate in planning sessions to develop data pipeline architecture.
• Design data ingestion, transformation, and storage workflows that are scalable.
• Implement data quality checks and validation procedures.
• Conduct comprehensive testing, including unit, integration, and system testing.
• Debug and resolve complex software defects and issues.
• Document data engineering processes, pipeline configurations, and data flows.

• Design, build, and operate batch and streaming data pipelines on modern cloud data platforms
• Develop robust ETL/ELT processes using SQL, Python, and PySpark
• Partner with analysts and data scientists to deliver trusted, production-grade data assets
• Lead the implementation and evolution of data governance practices

Senior Data Engineer – Governance Focus

• Define and implement architectural solutions that ensure the delivery speed and scalability of data pipelines
• Lead the adoption of managed and serverless cloud offerings to create performant and scalable cloud-native application solutions
• Drive best practices for advanced ETL/ELT workflows, data governance, and data quality across platforms
• Serve as a technical leader, defining and enforcing data security and access control policies across all data pipelines and platforms
• Ensure robust systems across heterogeneous technology environments and BI integrations

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

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